In [1]}, hyper-parameters optimization for object tracking algorithms is modeled as RL and Normalized Advantage Functions [2]} is used to learn a policy network that receives a state and returns the optimal hyper-parameters settings for a particular object tracking algorithm. Most of these algorithms produce a heat map in the search region showing the probable location of objects. The combination of this heat map, the parameters of the object tracking algorithm and the appearance features like RGB-color constitute the state, and the reward is the tracking accuracy.
